Abstract
The control design of target search and hunting using multi-robot remains a challenge in recent years. In this paper, we propose a control algorithm of multi-robot for target search and hunting inspired by potential field-based particle swarm optimization (PPSO). Firstly, a potential field function is established according to the initial positions of the obstacles, un-search area and targets. Then, the fitness function of PSO's (particle swarm optimization) is determined by the potential function of the work area. Lastly, multi-robot start performing target search and hunting missions driven by the proposed PPSO algorithm. Simulation results demonstrate that the PPSO algorithm is applicable and feasible for multi-robot cooperation to search and hunting targets. Compared with other commonly used methods for control of multi-robot, simulation results indicate that the PPSO algorithm has more stability and higher efficiency.
Zusammenfassung
Der optimale Steuerungsentwurf miteinander kooperierender Roboter ist eine Herausforderung in den letzten Jahren. In dem Beitrag wird hierzu ein Steuerungsalgorithmus für miteinander kooperierende Roboter vorgeschlagen, der auf einer Potentialfeld-basierten Partikelschwarmoptimierung (PPSO) basiert. Zuerst wird eine Potentialfeldfunktion festgelegt, die die Anfangspositionen der Hindernisse, der Nichtsuchgebiete und der Ziele enthält. Anschließend wird eine Fitnessfunktion für die Partikelschwarmoptimierung für das Suchgebiet bestimmt, in der die Potenzial-Funktion implementiert ist. Zum Schluss wird der vorgeschlagene PPSO Algorithmus auf die Optimierung von miteinander kooperierenden Robotern angewendet. Simulationsergebnisse zeigen, dass der PPSO-Algorithmus anwendbar für die Optimierung miteinander kooperierender Roboter ist. Der Vergleich mit den Simulationsergebnissen bisher hierfür eingesetzter Methoden zeigt, dass der PPSO-Algorithmus eine bessere Stabilität und eine höhere Effizienz hat.
About the authors
Xiang Cao was born in Sichuan, China. He received his B.Sc. degree in electronic and information engineering from Southwest University, Chongqing, China, in 2004, and his M.Sc. degree in communication and information systems from Shanghai Maritime University, Shanghai, China, in 2011, and his Ph.D. degree in power electronics and power transmission from Shanghai Maritime University, Shanghai, China, in 2016. Since 2016, he is doing postdoctoral research at Southeast University. He is currently a lecturer in the School of Physics and Electronic Electrical Engineering, Huaiyin Normal University. His current research interests include target searching and path planning of underwater vehicles.
School of Automation, Southeast University, 2 Sipailou, 210096 Nanjing, China; and School of Physics and Electronic Electrical Engineering, Huaiyin Normal University, 223300 Huaian, China
Changyin Sun received his bachelor's degree from the College of Mathematics, Sichuan University, Chengdu, China, and his M.S. and Ph.D. degrees in electrical engineering from Southeast University, Nanjing, China, respectively, in 2001 and 2004. He is currently a Professor with the School of Automation, Southeast University, Nanjing, China. Dr. Sun is an Associate Editor of the IEEE Transactions on Neural Networks and Learning Systems, the IEEE Neural-Processing Letters, and the IEEE/CAA Journal of Automatica Sinica. His current research interests include intelligent control, flight control, pattern recognition, and optimal theory.
School of Automation, Southeast University, 2 Sipailou, 210096 Nanjing, China
©2017 Walter de Gruyter Berlin/Boston
